Tag: Generative AI Tools

Forging a Personal Chatbot with OpenAI API, Chroma DB, HuggingFace Spaces, and Gradio 🔥

If you have checked the Internet in 2023 , you’re likely familiar with Generative AI . The launch of ChatGPT has sparked a surge in interest , investment , and innovative projects focused on Large Language Models (LLMs), Artificial General Intelligence (AGI), and Retrieval Augmented Generation (RAG). Yet, navigating this burgeoning field can be challenging : What tangible benefits does Generative AI offer? How complex is it to develop a Generative AI application? What kind of performance can one expect from such applications in real-world projects? In this article, I outline a project I developed to field questions about my resume and deepen my understanding of Generative AI.

Framework, Template, or Example? 🤔 Choosing the Right AI Starter Kit for Your Team ✨

Jumpstarting AI development can be tricky, but AI starter kits can provide the necessary launchpad. The article explores three main types: Frameworks offer a highly structured, guided approach suited for mature domains and consistency; Templates provide a standardized project setup with more flexibility, ideal for diverse projects with common delivery needs; and Examples offer simple, working code illustrations, best for quickly exploring new or rapidly changing areas like Generative AI. The key is choosing the right type based on your team's needs and the specific project context to build AI applications more efficiently.

BKFC: An Agentic Workflow for Gathering Knowledge from Google Chat

This article introduces BKFC (Build Knowledge From Chats), a Python notebook designed as an agentic workflow to tackle the common problem of extracting useful information from cluttered Google Chat histories. The author explains how manually searching through chats is inefficient. BKFC automates this by fetching recent messages via the Google Chat API, processing them, and then using Vertex AI's Gemini model for analysis. Crucially, it prompts Gemini to return structured insights (like summaries, Q&A, action items, project updates) based on a predefined Pydantic schema. The tool demonstrates a practical way to use AI (specifically Gen AI and APIs) to turn conversational data into organized, actionable knowledge, saving time and improving team awareness.

Hackathon Speedrun: Build & Deploy a RAG App in Minutes with Vertex AI Studio & Vertex AI Search!

This article provides a quick guide for building and deploying a Retrieval Augmented Generation (RAG) application in minutes, perfect for hackathon environments. It details how to leverage Google Cloud's Vertex AI Studio and Vertex AI Search to create a grounded Large Language Model (LLM) application that can answer questions based on a specific knowledge base, such as company documentation. The process involves preparing data in Google Cloud Storage, creating a searchable data store with Vertex AI Search, crafting and grounding a prompt in Vertex AI Studio, and then deploying the AI assistant as a web app using Cloud Run. The guide emphasizes speed and automation, allowing users to focus on data and user experience rather than complex technical setups.

The Great Data Divergence: Why Generative AI Demands a New Approach Beyond the Data Lake

The slow, batch-processing nature of the data lake is obsolete for modern Generative AI, which requires instant access to fresh data. In this article, the author proposes a shift away from centralizing data, advocating instead for an API-first approach. This allows AI applications to directly and quickly access live data from its source, enabling truly real-time, responsive features.

August 11, 2025

The Agentic Cloud: Forging the Next Era of Infrastructure

A new paradigm called the "Agentic Cloud," driven by Generative AI, is set to disrupt the dominance of cloud hyperscalers. While today's giants like AWS and Azure rely on vast but complex service catalogs, this emerging model uses intelligent agents to translate high-level user intentions into fully provisioned and autonomously managed infrastructure. This approach threatens to commoditize the underlying cloud platforms, shifting the primary value from the provider's software ecosystem to the intelligence of the agent itself. Despite significant technical challenges in reliability and security, the Agentic Cloud promises to democratize access to elite infrastructure, enabling a new wave of innovation by shifting the industry from a catalog-driven to an intelligence-driven model.

October 7, 2025

It’s Not Artificial: Recreating a Conversational Format with Gemini’s Multi-Speaker Text to Speech

Inspired by the French show “C’est pas sorcier,” “It’s Not Artificial” uses Google’s Gemini multi-speaker text-to-speech to recreate its conversational learning style. The project automatically generates full audio episodes — complete with distinct voices, languages, and topics — from simple inputs. What began as a nostalgic experiment now hints at a future where AI-driven conversations make education and training more personal, engaging, and human-like.

October 21, 2025

Slides-To-Translate: When IT Says No, Build a $0.04 Solution on Your Lunch Break

When IT blocked every translation tool, Médéric Hurier decided not to wait. In just one lunch break, he built Slides-To-Translate — a fully automated Google Slides translator using Gemini 2.5 Flash, Colab, and Vertex AI — for only $0.04. His quick hack turned a bureaucratic bottleneck into a lightning-fast, secure, and reusable solution that proves anyone with a bit of code and curiosity can outpace corporate constraints.

November 4, 2025

Deploying AI Agents in the Enterprise without Losing your Humanity using ADK and Google Cloud

Deploying AI agents in enterprises is complex, balancing security, scalability, and usability. This post compares deployment paths on Google Cloud—highlighting Cloud Run with IAP as the most secure and flexible option—and shows how teams can build powerful agents with ADK without losing the human touch.

November 18, 2025

Combo-Banana: Building Custom Image Workflows in Record Time

Combo-Banana is an open-source prototype based on Google's Nano Banana, designed to empower product designers by allowing them to quickly define and automate their multi-step image editing workflows. This tool transforms tedious, repetitive tasks—like background removal, color correction, and integration into new scenes—into a fast, consistent, and automated process, freeing designers to focus on creative work.

December 16, 2025

Da2a: The Future of Data Platforms is Agentic, Distributed, and Collaborative

The traditional centralized data platform, characterized by rigid data warehouses and complex ETL pipelines, creates technical bottlenecks that severely slow down the delivery of business insights, forcing decision-makers to wait for overburdened data engineering teams. The open-source prototype Da2a proposes a radical new paradigm: a distributed, agentic ecosystem where specialized, autonomous agents (e.g., Marketing, E-commerce) manage their own domain data and collaborate via an Agent-to-Agent (A2A) protocol to answer complex, cross-domain queries. Instead of focusing on the engineering of data movement and storage, this approach is insight-focused, allowing an orchestrator agent to plan and delegate tasks, abstracting underlying complexity and enabling greater scalability, extensibility, and alignment with high-level business logic—a critical evolution for MLOps engineers looking to build more flexible and responsive data foundations.

February 10, 2026

Finding the Holy Grail of AI Agent UIs: From AI-Orchestrated Development to A2UI

Addressing the challenge of AI agent exposition, this post evaluates various implementation paths, including full-stack frameworks and AI-generated code. It identifies A2UI as a promising declarative solution that enables dynamic, secure interfaces by decoupling the agent's logic from the client's rendering capabilities.

MLOps Coding Skills: Bridging the Gap Between Specs and Agents

This article explores how to use "Agent Skills"—simple Markdown-based context modules—to ensure AI agents strictly adhere to your team's MLOps practices and tooling preferences. By providing explicit organizational rules upfront, developers can eliminate generic boilerplate and align AI-generated code with production-grade standards.

mAIdAI: Building a Personal Assistant with Google Cloud and Vertex AI

mAIdAI is a lightweight personal AI assistant built with Google Chat, Cloud Run, and Vertex AI, designed to automate repetitive micro-tasks. By grounding the model with a local markdown context file, it provides highly personalized workflow assistance directly within your chat environment.

Chaigent: An affordable alternative to Gemini Enterprise on Google Cloud

Chaigent combines Chainlit and Vertex AI to deliver a code-first, serverless AI agent platform that avoids costly per-seat licensing fees. It empowers developers to build highly customizable, enterprise-grade agents using a scalable pay-as-you-go architecture.

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